{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as  np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000000 entries, 0 to 9999999\n",
      "Data columns (total 24 columns):\n",
      "id                  float64\n",
      "click               int64\n",
      "hour                int64\n",
      "C1                  int64\n",
      "banner_pos          int64\n",
      "site_id             object\n",
      "site_domain         object\n",
      "site_category       object\n",
      "app_id              object\n",
      "app_domain          object\n",
      "app_category        object\n",
      "device_id           object\n",
      "device_ip           object\n",
      "device_model        object\n",
      "device_type         int64\n",
      "device_conn_type    int64\n",
      "C14                 int64\n",
      "C15                 int64\n",
      "C16                 int64\n",
      "C17                 int64\n",
      "C18                 int64\n",
      "C19                 int64\n",
      "C20                 int64\n",
      "C21                 int64\n",
      "dtypes: float64(1), int64(14), object(9)\n",
      "memory usage: 1.8+ GB\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('data/train.csv',nrows=10000000)\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'click', 'hour', 'C1', 'banner_pos', 'site_id', 'site_domain',\n",
       "       'site_category', 'app_id', 'app_domain', 'app_category', 'device_id',\n",
       "       'device_ip', 'device_model', 'device_type', 'device_conn_type', 'C14',\n",
       "       'C15', 'C16', 'C17', 'C18', 'C19', 'C20', 'C21'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>click</th>\n",
       "      <th>hour</th>\n",
       "      <th>C1</th>\n",
       "      <th>banner_pos</th>\n",
       "      <th>site_id</th>\n",
       "      <th>site_domain</th>\n",
       "      <th>site_category</th>\n",
       "      <th>app_id</th>\n",
       "      <th>app_domain</th>\n",
       "      <th>...</th>\n",
       "      <th>device_type</th>\n",
       "      <th>device_conn_type</th>\n",
       "      <th>C14</th>\n",
       "      <th>C15</th>\n",
       "      <th>C16</th>\n",
       "      <th>C17</th>\n",
       "      <th>C18</th>\n",
       "      <th>C19</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1.000009e+18</td>\n",
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       "      <td>50</td>\n",
       "      <td>1722</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>-1</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000017e+19</td>\n",
       "      <td>0</td>\n",
       "      <td>14102100</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "      <td>1fbe01fe</td>\n",
       "      <td>f3845767</td>\n",
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       "      <td>7801e8d9</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000037e+19</td>\n",
       "      <td>0</td>\n",
       "      <td>14102100</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "      <td>1fbe01fe</td>\n",
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       "      <td>50</td>\n",
       "      <td>1722</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>100084</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000064e+19</td>\n",
       "      <td>0</td>\n",
       "      <td>14102100</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "      <td>1fbe01fe</td>\n",
       "      <td>f3845767</td>\n",
       "      <td>28905ebd</td>\n",
       "      <td>ecad2386</td>\n",
       "      <td>7801e8d9</td>\n",
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       "      <td>1</td>\n",
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       "      <td>15706</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>1722</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>100084</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000068e+19</td>\n",
       "      <td>0</td>\n",
       "      <td>14102100</td>\n",
       "      <td>1005</td>\n",
       "      <td>1</td>\n",
       "      <td>fe8cc448</td>\n",
       "      <td>9166c161</td>\n",
       "      <td>0569f928</td>\n",
       "      <td>ecad2386</td>\n",
       "      <td>7801e8d9</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18993</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>2161</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>-1</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  click      hour    C1  banner_pos   site_id site_domain  \\\n",
       "0  1.000009e+18      0  14102100  1005           0  1fbe01fe    f3845767   \n",
       "1  1.000017e+19      0  14102100  1005           0  1fbe01fe    f3845767   \n",
       "2  1.000037e+19      0  14102100  1005           0  1fbe01fe    f3845767   \n",
       "3  1.000064e+19      0  14102100  1005           0  1fbe01fe    f3845767   \n",
       "4  1.000068e+19      0  14102100  1005           1  fe8cc448    9166c161   \n",
       "\n",
       "  site_category    app_id app_domain ...  device_type device_conn_type    C14  \\\n",
       "0      28905ebd  ecad2386   7801e8d9 ...            1                2  15706   \n",
       "1      28905ebd  ecad2386   7801e8d9 ...            1                0  15704   \n",
       "2      28905ebd  ecad2386   7801e8d9 ...            1                0  15704   \n",
       "3      28905ebd  ecad2386   7801e8d9 ...            1                0  15706   \n",
       "4      0569f928  ecad2386   7801e8d9 ...            1                0  18993   \n",
       "\n",
       "   C15  C16   C17  C18  C19     C20  C21  \n",
       "0  320   50  1722    0   35      -1   79  \n",
       "1  320   50  1722    0   35  100084   79  \n",
       "2  320   50  1722    0   35  100084   79  \n",
       "3  320   50  1722    0   35  100084   79  \n",
       "4  320   50  2161    0   35      -1  157  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                  False\n",
       "click               False\n",
       "hour                False\n",
       "C1                  False\n",
       "banner_pos          False\n",
       "site_id             False\n",
       "site_domain         False\n",
       "site_category       False\n",
       "app_id              False\n",
       "app_domain          False\n",
       "app_category        False\n",
       "device_id           False\n",
       "device_ip           False\n",
       "device_model        False\n",
       "device_type         False\n",
       "device_conn_type    False\n",
       "C14                 False\n",
       "C15                 False\n",
       "C16                 False\n",
       "C17                 False\n",
       "C18                 False\n",
       "C19                 False\n",
       "C20                 False\n",
       "C21                 False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>1.000055e+19</td>\n",
       "      <td>14103100</td>\n",
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       "      <td>22676</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
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       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000109e+19</td>\n",
       "      <td>14103100</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
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       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000138e+19</td>\n",
       "      <td>14103100</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "      <td>85f751fd</td>\n",
       "      <td>c4e18dd6</td>\n",
       "      <td>50e219e0</td>\n",
       "      <td>9c13b419</td>\n",
       "      <td>2347f47a</td>\n",
       "      <td>f95efa07</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23160</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>2667</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>-1</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id      hour    C1  banner_pos   site_id site_domain  \\\n",
       "0  1.000017e+19  14103100  1005           0  235ba823    f6ebf28e   \n",
       "1  1.000018e+19  14103100  1005           0  1fbe01fe    f3845767   \n",
       "2  1.000055e+19  14103100  1005           0  1fbe01fe    f3845767   \n",
       "3  1.000109e+19  14103100  1005           0  85f751fd    c4e18dd6   \n",
       "4  1.000138e+19  14103100  1005           0  85f751fd    c4e18dd6   \n",
       "\n",
       "  site_category    app_id app_domain app_category ...  device_type  \\\n",
       "0      f028772b  ecad2386   7801e8d9     07d7df22 ...            1   \n",
       "1      28905ebd  ecad2386   7801e8d9     07d7df22 ...            1   \n",
       "2      28905ebd  ecad2386   7801e8d9     07d7df22 ...            1   \n",
       "3      50e219e0  51cedd4e   aefc06bd     0f2161f8 ...            1   \n",
       "4      50e219e0  9c13b419   2347f47a     f95efa07 ...            1   \n",
       "\n",
       "  device_conn_type    C14  C15  C16   C17  C18  C19     C20  C21  \n",
       "0                0   8330  320   50   761    3  175  100075   23  \n",
       "1                0  22676  320   50  2616    0   35  100083   51  \n",
       "2                0  22676  320   50  2616    0   35  100083   51  \n",
       "3                0  18648  320   50  1092    3  809  100156   61  \n",
       "4                0  23160  320   50  2667    0   47      -1  221  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('data/test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4577464 entries, 0 to 4577463\n",
      "Data columns (total 23 columns):\n",
      "id                  float64\n",
      "hour                int64\n",
      "C1                  int64\n",
      "banner_pos          int64\n",
      "site_id             object\n",
      "site_domain         object\n",
      "site_category       object\n",
      "app_id              object\n",
      "app_domain          object\n",
      "app_category        object\n",
      "device_id           object\n",
      "device_ip           object\n",
      "device_model        object\n",
      "device_type         int64\n",
      "device_conn_type    int64\n",
      "C14                 int64\n",
      "C15                 int64\n",
      "C16                 int64\n",
      "C17                 int64\n",
      "C18                 int64\n",
      "C19                 int64\n",
      "C20                 int64\n",
      "C21                 int64\n",
      "dtypes: float64(1), int64(13), object(9)\n",
      "memory usage: 803.2+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f2293f9438>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['click'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f1a4440240>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['banner_pos'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f1a44f9240>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['site_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f223e63f98>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['site_domain'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f238badc88>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['site_category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f1a45e3da0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['app_category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp =sns.countplot(train['device_conn_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'07d7df22': 6292277,\n",
       "         '0f2161f8': 2402914,\n",
       "         'cef3e649': 651844,\n",
       "         '8ded1f7a': 243683,\n",
       "         'f95efa07': 287318,\n",
       "         '75d80bbe': 9446,\n",
       "         '4ce2e9fc': 5758,\n",
       "         'd1327cf5': 38793,\n",
       "         '09481d60': 42030,\n",
       "         'fc6fa53d': 6185,\n",
       "         'dc97ec06': 5146,\n",
       "         'a3c42688': 2955,\n",
       "         '0f9a328c': 1967,\n",
       "         '879c24eb': 5925,\n",
       "         'a86a3e89': 918,\n",
       "         '7113d72a': 57,\n",
       "         'a7fd01ec': 121,\n",
       "         '4681bb9d': 1246,\n",
       "         '8df2e842': 419,\n",
       "         '2281a340': 503,\n",
       "         '18b1e0be': 54,\n",
       "         '0bfbc358': 222,\n",
       "         '79f0b860': 187,\n",
       "         '4b7ade46': 4,\n",
       "         '86c1a5a3': 2,\n",
       "         '2fc4f2aa': 5,\n",
       "         '5326cf99': 14,\n",
       "         'ef03ae90': 1,\n",
       "         '71af18ce': 2,\n",
       "         '52de74cf': 1,\n",
       "         'bd41f328': 1,\n",
       "         'f395a87f': 1,\n",
       "         'cba0e20d': 1})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "counts = Counter(train['app_category'])\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = train['app_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['07d7df22', '0f2161f8', 'cef3e649', 'f95efa07', '8ded1f7a', '09481d60',\n",
       "       'd1327cf5', '75d80bbe', 'fc6fa53d', '879c24eb', '4ce2e9fc', 'dc97ec06',\n",
       "       'a3c42688', '0f9a328c', '4681bb9d', 'a86a3e89', '2281a340', '8df2e842',\n",
       "       '0bfbc358', '79f0b860', 'a7fd01ec', '7113d72a', '18b1e0be', '5326cf99',\n",
       "       '2fc4f2aa', '4b7ade46', '86c1a5a3', '71af18ce', 'cba0e20d', 'ef03ae90',\n",
       "       '52de74cf', 'bd41f328', 'f395a87f'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f23e8de9b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.pivot_table('click', index='banner_pos', columns=['device_conn_type']).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f2245cb9e8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.pivot_table('click', index='banner_pos', columns=['device_type']).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 448593,       0, 9356219,       0,       0,       0,       0,\n",
       "              0,  171529,   23659], dtype=int64),\n",
       " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(train['device_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "site_category\n",
       "0569f928     True\n",
       "110ab22d     True\n",
       "28905ebd     True\n",
       "335d28a8     True\n",
       "3e814130     True\n",
       "42a36e14     True\n",
       "50e219e0    False\n",
       "5378d028     True\n",
       "70fb0e29     True\n",
       "72722551     True\n",
       "74073276     True\n",
       "75fa27f6     True\n",
       "76b2941d     True\n",
       "8fd0aea4     True\n",
       "9ccfa2ea     True\n",
       "a818d37a     True\n",
       "bcf865d9     True\n",
       "c0dd3be3     True\n",
       "c706e647     True\n",
       "dedf689d     True\n",
       "e787de0e     True\n",
       "f028772b     True\n",
       "f66779e6     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.pivot_table('click', index='app_category', columns=['site_category']).isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "site_category\n",
       "0569f928     True\n",
       "110ab22d     True\n",
       "28905ebd     True\n",
       "335d28a8     True\n",
       "3e814130     True\n",
       "42a36e14     True\n",
       "50e219e0    False\n",
       "5378d028     True\n",
       "70fb0e29     True\n",
       "72722551     True\n",
       "74073276     True\n",
       "75fa27f6     True\n",
       "76b2941d     True\n",
       "8fd0aea4     True\n",
       "9ccfa2ea     True\n",
       "a818d37a     True\n",
       "bcf865d9     True\n",
       "c0dd3be3     True\n",
       "c706e647     True\n",
       "dedf689d     True\n",
       "e787de0e     True\n",
       "f028772b     True\n",
       "f66779e6     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.pivot_table('click', index='app_id', columns=['site_category']).isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "site_category\n",
       "0569f928     True\n",
       "110ab22d     True\n",
       "28905ebd     True\n",
       "335d28a8     True\n",
       "3e814130     True\n",
       "42a36e14     True\n",
       "50e219e0    False\n",
       "5378d028     True\n",
       "70fb0e29     True\n",
       "72722551     True\n",
       "74073276     True\n",
       "75fa27f6     True\n",
       "76b2941d     True\n",
       "8fd0aea4     True\n",
       "9ccfa2ea     True\n",
       "a818d37a     True\n",
       "bcf865d9     True\n",
       "c0dd3be3     True\n",
       "c706e647     True\n",
       "dedf689d     True\n",
       "e787de0e     True\n",
       "f028772b     True\n",
       "f66779e6     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.pivot_table('click', index='app_domain', columns=['site_category']).isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f23a78f940>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.pivot_table('click', index='banner_pos', columns=['device_type']).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f2246df438>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.pivot_table('click', index='site_category', columns=['device_type']).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device_type\n",
       "0     True\n",
       "1    False\n",
       "4     True\n",
       "5     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.pivot_table('click', index='site_category', columns=['device_type']).isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "site_category\n",
       "0569f928     True\n",
       "110ab22d     True\n",
       "28905ebd     True\n",
       "335d28a8     True\n",
       "3e814130     True\n",
       "42a36e14     True\n",
       "50e219e0    False\n",
       "5378d028     True\n",
       "70fb0e29     True\n",
       "72722551     True\n",
       "74073276     True\n",
       "75fa27f6     True\n",
       "76b2941d     True\n",
       "8fd0aea4     True\n",
       "9ccfa2ea     True\n",
       "a818d37a     True\n",
       "bcf865d9     True\n",
       "c0dd3be3     True\n",
       "c706e647     True\n",
       "dedf689d     True\n",
       "e787de0e     True\n",
       "f028772b     True\n",
       "f66779e6     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.pivot_table('click', index='device_type', columns=['site_category']).isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f224f30710>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JRREEQRDUEooiCIIgqCUURRAEQVBLKIogCIKgllAUQRAEQS0vZx7F/zT37baz/85tW/28CSNxKUEQBMNCWBRBEARBLaEogiAIglpCUQRBEAS1hKIIgiAIaglFEQRBENQSiiIIgiCoJRRFEARBUEsoiiAIgqCWUBRBEARBLaEogiAIglpCUQRBEAS1RK2nIeLM42/s2LbXwVsM+3UEQRD8t4RFEQRBENQSFsUI8/Cdx/jv3LaV3vKVkbmYIAiCEsKiCIIgCGoJRREEQRDUEooiCIIgqCUURRAEQVDLfBHMljQGOANYG3gB2M3MHhjZqwqCIPjfYH6xKHYAxprZ24GDgRNH+HqCIAj+Z5gvLApgE+BqADP7jaT1R/h6hp1Db7+/Y9vX3vr6jm27Hn9Dx7YLDn7nkFxTEAT/G/QNDAyM9DV0RdJ5wOVmdlX6/2FgVTObNbJXFgRBMPqZX1xPzwDjcv+PCSURBEEwPMwvimIysA2ApLcB94zs5QRBEPzvML/EKH4CvFvSrUAfsMsIX08QBMH/DPNFjCIIgiAYOeYX11MQBEEwQoSiCIIgCGoJRREEQRDUEooiCIIgqCUUxXyCpGUkrSpp6ZG+lv8GSQtLWif9vb2kBYf5/Iuk38v/l8cZK2kDSe+Q9CZJfXPnCucuL6fd9PJM/tvvcaiQtOz8+kwkfSj9fu1cOt9m6ffCL/cYozbrSdJawPP54oGSNjSz3+b+Hwt8BngJuBT4HrAksLeZ3V2Q2x/YGFgUeBy4DjjXzGbn5D6btjX6UiX1A28FFsTTflcwsx8UZN4KnA4sAPwHn3jYl67x1pzccWZ2mKTVge8DK+AL5+1sZveVnPvVwDeA8cBlwB8K382jwACwMLAI8HdgReAxM1u54n7eBwiYambXVMhcBlxvZmdJOhBYx8w+ltu/CrAGcCNe12s94I/A18zs6cKxGj2/nPxXgCXMbH9JlwJ3mNnxuf0PpnvOeAl/Ni+Y2Zo5uW2BY4D7gY2A3wCvAQ4ws1sK51wO2BRYAngKuM3MHi25j92A54GJZvZi2r6HmZ1dkH0jMNvM7pX05XS/3yr5bhq1m5z87sAbzGw/SdcC3zOz75XIHQPsBSyEt4v7zOyNRbluNH2nCp9ZnvZ35bbcvl3wZ/Bz4GL8u1wE+JyZXV84Th/wfuDfgAHfBmYDh5rZv0vOuxZwJv5dX4S375/n9q9UdZ9m9nBOrum7/Hvgk8D56Xdf7nhl73Jtm5A0BXgH8Avg3YXjvVh17XlGpUUh6QjgLOAiSWfmRhZfL4heDCwPrAX8Fq9Q+wXg5ILcOcCjwJfwL/s2vBGeXpD7BnCdpNUaXuqPgSPTcc7EO70i3wY+aGbrmdnmZrYusH3anuft6fdJwH5mtiL+QhevMX9PF+Av/M0U7tnMljezFYCrgNXNbHVgNfx76kDS6XijngXsJulbFed9tZmdlc7xTfz7zzMReC5dzyzgcOAf+LMq0vT5ZWxvZvunc38I2K6wfw3gDcCvgZ3MTMAHgVsKcgcAG5nZTsBb8Jd+KwrtS9JueMe1MfBavGbZlZL2LLnnFYDVgVskLZW2f6RwvGPwdv09SZfjSv5xYELJvTZtNxl7AYekv7cFPlch9158wHARsCb+bEqRtJakSZLukXRwGkhkNH2nsmNdAPwKmAT8ruQ+PocXC/0W8H4zWwfYgs53HuBUvNDokcA16ZjXAOdV3MrJ+Nytx/HO+6jC/h+mn+vxdnh+up+fFuSaPpNzge/gg66zcz9nFS+sYZu4DrgL2BBXjPfmfjdiVCoKfBb3pma2If4SZ42vaIoubWaHm9kXgRfN7JdmdmfJ8V5rZheY2b2pc3unmZ0EFEdSd+Ed2w8kfVfS2zuO1M4SZvZevHGtB4wtkVnQzP5e2PZ32ke+eRYxs8kAaVRd5UYYa2Y3AANmZvgIrIxVs/Ob2T+BqtHT2mb2ETM7OXXCG1fIkaweJL0OH13lmW1mNwKrmNlXzewuMzsFH5EXafr8MuZIWiide0EK7d/MXjCz54HXmdmUtO1O/IXNswQwJ/39PLCamT2DW195dgE2NrMvmdmRZvYl3AIpDgiWM7NDzexAvGP7WXITFNvrlma2Kf7dvtnMDjKz7+AjyCK9tpvZ6d4xs5dq5J4wsxeAcclaX6RCDuo72KbvVMYaad81uIIqtteXzOxZYAbw13Qf/6y4j7XNbBe8k17SzM43sx/V3Uu61wEzm5bOkd/39lTZ+o/4oOrduNJ/pHCYRs/EzM5MxzgQeJ+ZvQP4uJmVVffs2ibM7DAzez1ula+aflYxs1Wr7rfI/DIzu1f6LLl/zOwASRdJOoCSRpNGd0sDS0vaEq8r1WH6SvoIXsH2/cDMZI4WO/YBM/sN8FZJ2wH7Svoe8EwaPRR5Kf1e1MyeyzqxAr+QdD1wLfA0sDjwHuCXBbnVJV0BLCHpg8DPgH1xRVnGC5K2AhZIZVGqFMWf0j1MwTu5SRVyD0ta0cwekfRK/AUoYx/gR0nmH0BxdP2UpP/D7/tTwJW44p9ZdrCmzy9xFjBV0j14x/ONCrmnJH2V1j0/VNh/CTBF0o3AZsDpyY32+4LcgsAraD1n8M6o2A4XkrSsmT1uZj9JvumL6FQ8C0oSsCywrKRXAc+mcxRp2m4yrpA0Kd3zusAVFXKPSNoVeFbS19NxKzGzByQNmNk0SW0dbMN3KmOGmQ1IWtTMHi95V36W2v9U4OeSrsGtn85yyn7ujc1scmozJC9AlQ//SUl7AItK2gl3IZaxopnNSPf9rKQVCvt7fSYrAF/FXXQnS/qdmRXbbNc2Ia9osZuZfbXiPF0ZrYrih8kv914zexLYFe8431aQ+ySwH3An8AncbH0S+HxB7tNp31eS7OdxX9/eBbm87+9KvJND0rIV1/mT5Ca7W9Jv8E6uDTM7RtJbcLfFsngDO8jMip3SWsAyuGXyGP5sl0n3VcZngRPSMb+Mux6q5LbGR3MXp/saJBfLGAvsKK/suyI+iszLfTeN4t6S3AJV7A58Ex8hrZyOcwvuwy+SPb+7qH9+AJjZ+ZJ+BqwKPGBmT1Rcw8eBndN934tbifnjfEPSL/CR7dnmvuFlSo73VeAOSffT6hhWw90teY4AJknawsz+bWbfkbQona6xQ4Af4ArpaOBufHS7f8m9Nm03mfyxkn6OW08TrSTGk9gDjwVcin9HO1XIQX0Hm71TR+DPr+qdyrgj+d//KekSCn2XmR0vaXPcBfgwsBxwipn9ouIejpN0q7ViCCfiLsUyPgMcirfF9Sl3EQNcK+km3DW2AR4zy19j/pksg7/vlc8Ed6Gtlz77IUmT6RzcVLWJL+dklgLOT7GnEzJl1gujOZi9CvCwtQebdzCzn+b+v9rM3ivpSDM7uodjv9nM/lCy/ZVWEgzrcqy+NFJ6E955PVfYvxRuxk7BX8z18VHTuZaroCvpZjPbTNKZZlbV6XfcRzr2VDPr8FdKWht4ApiGN8jZwIlmVjq673KuO3Ef7ocoxBvM7NAS+WVJAeCaDr34mRVw6+z+wvaqoPfnis9R0kb4i5wFWK83sz+XnGvbdLwb8bjQkngw9OGCXD+uUBbHO4Y/W8PKx5KWM7PHKvaNwUecj1lJQLJpu8nJd0tuaBxwz31mcbyDfRPwZ9z18WRu/yuAN+NW1uN4O6zskCSNw+NXWwO/rfpukuynzGxi1f6KzyyY3G7F7a8DNjCzH0g6HjjLzB6qOMYb8TjXfWXKVtLH8OSG7J6vM7OrK451O+66fDG5Sm9OLq66e1gCeC7fJiTdgFsuX8QHfjfhsce/lvVjZYxKi0LSeHxk8rykb+c6mrVpDzAtLs9+2TSZb4NYeybOewqn+GZyNWBm1+a2z5L0HWBLvGN4CnfVHJ1v1JL+CXzKzK7PuciqKuJegrtMjsdHIT/H3R0X4iPfjOdSw3p96uDBLZwBM9uoeFBJh+Om+e3AfpIuTb7N/P534p31o7glNRMP+H2s5HhvTNdZmhkC7Ih3wO/DA2mlqD0zZAYwLnWKHdk6qVM/BXgRt46Oxp/5980sH9C+GPgT/ky+jLvkHk2f3SJ3vEPxTn1yutZ78ZHn9WZ2Rk7uPNyCGpfO+T3gn3gQcqv8NaZO+Z70uYvz7Sp3vKIb5Vp8dP1UQe58M/uMpA3x7/hxvA3vku/UE03bTcY5+Kj6CDy54ULaLfCJwAN4n3GLpK3MbDoecC9VFGb2TBoFPw7cU1AS2+Lf3QN4IsZvgddI6sgcS/Kr4YOMLOvpfbhlkO3/WuEjH5O0RrqOjoFI+syeuHXXn475Eq5ci0wEDkt//xKPt7wrd5yxuAv1XbSy2yZJui8/8JN0Mm7ZXYFbi08D2yQ32BEl5+3qKk1t4QxcgR6cfXeSfmJmOyaxvtQOT5J0Kt4/bYkPnopWaymjUlHgD/Yn+P3dLGkbM/sbsHlBbmt8tLMaFY098Q08eHk33qBeCXwUd7nkFcWFeKfxFVInh/vXL8YfTMa/gX0kfRJXIn+tOffCyW/9xRTUAvhp8jsW72WFdB970RkILbINsImZzUmj3lvwTIvB/Wa2kaTF8Jf8fQCSfl1xvFPwwOW5+It0Fd45AWBmD0n6F/A3/LlUjSCzzJDBGIc8/fBSPGsjzwm462MJ/Dmsgvtnb6E982lpMzs8HWuqmf0y/V28h/eaWZZzfi7wIzP7QOrszsjJrZ6stz7gj5kSkbRP/mDJDZe9Y314HOVRXHnn/deP4SP1mUnuVcB9ePvKBxxXSb+PA7Y2s/uTFfUDOtt203aTMdbMbpB0uJmZpGLMajkz+3C6rx3xmMCW1LQzeSbcMngG0G6StjSzzL1zAD5afkHSMrgbais8A2rTksNNxF25m+BKebHC/mVw9+vZ6Zqeo2ZAktgN/94Ox9vXvlWC5gkWmNnNaeCS57u4++wwWu/91vh7v2NObh0zy57T1ZKuNLPtJHUoxnSuvKv0L2b2eInYSXhftCCe+XRwGrzmExzuyh3zJfzdvKrqXssYrVlPC5vZOekF3h0P1C1JoVGb2dNJA29gZjflfwrH2xhXEpPN/ez3mtkuZrZrQW5xM/uhmT1jZgPp9yV0Bsmmm9l2+MjiEknXSNpX0vtL7uUlSRsAk9WaOLMxhYCtmc0xs0dwv/gOSTF+DQ/0lvEYrSyPhXD3Up4xklYys/+Q/NDpO6wKNtZmhqQR5GTc0rsQH7nemu4lTy/ZOgukc07FEwaeSa7GOUVBSXsmi2FpSVum77QY9F5MrUlOr0uyr6AzG2ZBeSLAx4BXSlojuW6KGWafwF0/65nZ8sCt1ko7zvM23K+9vZmtAvzG6rNSZmfuNfPMnrL3uFG7ydEtuWGh5A7EzH4CXE55wD3P2ma2k5VnwjXNHMuYaWZfBx4xs53xwdogZrYHrkw2x1NVHzWzC83swprre9x8Tsu4pAiq3pWnJH1WPrHyMxTaNj6n4xtm9gczezD9/gYeG8ozNlkASNoU6JcHnxetusD0Lu1ToSTAs73uM7M/4oO/E+Vu7IHcMfbLf0DS8pI611GuYbRaFP2S3mRm95jZrfLsjJ9RGIXkTf6i+Z/38Zn75HeVtL+kM6n+3h6TT+q6Gjcrx+E56Y8W5PrScX8M/FjSmrjF8e50nXn2wkdJywGHyjNHjPLgLvjIfuf09xF4PvVmufu8DW9EywH3S7ob96kW4wAHAJerfZLiz3DlU0a3zJBs7kG3EWQxM2Qc7iIrGwHdkkbIM4EHJE3Es7yKftemQe+DcQv0SVw5fAo4iM7c/j3xHPzf44rvJvz72z0vlEaffwXOkXQCFSmn5sHwjya5n1fJAUtKugP/jj+Dd9Qn4lZakT1xd1LTdtMtuaFpwD1PXSbcD2iWOZbRlzrVxdJ5Ozp1Mzs7teef0r4iZhVPS9oBGEhtd3yF3M64tbAj7sIsDhCfl2fo5d/7bejMONwTf8YrAn9Jx/ko/t0OUmKJLlVhiQI8I+mLeFLFv+QxkB+RU7jyNP1T6XTRXpR3N9cxWhXFF4BTJX0kNeofyoNBxYlYU/EX6UmSPz/3u2M0Z2YnSnon1Y3wE/gLdhCt4OVkPMMjT1vwyjxY2hEwTfseAN6V/KBLA09aynevYJaZ/Sl99q+SiqPrYpZKds/F805t0Ii+AAAgAElEQVTCZ41ngdPlgXdYxaxZumeGlI4gVSgrYJ2ZITPwGc8d8yPMZ1i/GU+znYV37E/inVBe7u+SjsZHXzPxzDBUKJFgZtdKWhnvLJ8AljKzo0rOezfwgXSMxfHO46/J+irKPiJP9z2NzsmFebkZwEclHYVnFZXJrJu+r7Vx5TgHj3+cXyL7F3poN8karctgut1ys9PTZ45LLro21CwT7kFcgW+DzxG4VylFuOL8R+Md9ffTZ0sD1Wb2G0k7A/9Xcy8Zu+GW48HUZP6lUf2+8vjZEiXX+DHc3bwP/t4/Tcl7n9pw9k4tgc9JOsd8/keeT+Cxk73M7FFJv865D4tksgvjFQTukafH5wd0J1Ltom2kKEZl1pNSKqYKGRmSxpjZnNz/4/EJPO8yD8xVHa+nDJL0mRVoBcmK5QYaZyjJYwJVI9GOCTiSfoC/SLfhKXqrmllH8FLSqvjIevV0Lwdae1ygGDh9AleQZYHT/HHfY+0B/mz7QXhjvZE0gsQ75FXNrDiXguSGW9/MviLpauCk4nELz+XT+EtYlhG2G668x+Ajr2+m7TdUfIdbp+t7GncLfDa5JopyH8T92/34KG7AzI6t+W42Av6R3IKV5Dqk60v2LY2PVF/CFeMc4PtWiPXIS4cckOTOx6sAjMPz6UvnFnS5ppnAF8ysQym9HNRjmYqSzy9sPvGvuL1bUgWSNjWzSWkAtBewDnAHnSV5dsA70tm4pb4jbimbmR1UOOYStAYi2bbXlj3rJu0mWR1n4hbAkWXttOJ72RyYkwZ62bbfmtmGaZBxb3JvImmymVVOjM0zWi2KdeQlJD5UHDXio17ARwqSDsYnGP2q5ng9ZZBIOh/3Oy+GuzD+QnsGyUw1z1DKOtEjcZN6Mq4A3kc5u6TPbINbKVUd1/n4fIVb071cgLu+MhoFTuX1rfJ8SdJJAGZ2TrbR2ucenGVm1mUEeRTucgLPrLmK9sQBaP5cdqc143eCpEPN7GtUB2KPAjZM7eNV+PdenIMDPpJ7G24hHovHGQa/79Thnwv8Cw9sHgi8KOn0fIdb1SFJene+Q0rupiwYfDM+inwWD/IX3WjfxzuhJfDMu63wONTl5CahyTNqlil8NmuLeTfH3cBb5KmWR1tnHG+Q5Fo8HHenDmYB0Z79dx7tZSqyZzGAZ9tlx9oOt8ZeAg4zsx+mXVfl5XLUJlUkjk6f/Sb+jv4Yz1g6hfZ5HIfgSmQx/NmuZJ6qOrlwv4MDEUmDAxE8yF12jbXtBppbovLY35nAdDyteXM83rSZmR2XxJq6aCsZrYqiUSomdKS3VtFrBsmatDIwDsUfYJ5taGUoVdXUya7PYHCOxo/S5p9I+kJeTp4eeGwa0TQxJ2ebWeb3v1JSVcZHW+BUnRkfO+ATeq7CX/aFqWjYZjZV0lPA8cmau0xSW75+jpeyTsXMnpZU5vJq+lxmWyvv/1N4xkmxAGCeGcndQPL7Fl0DGXPMYy4D5nNhinIn42UiVsbjOyvgfuKbaHcXNeqQcIX3BtwSuyvryCXdXHJtC5vZeWn/rpbSryUVLeAdceW/mRXm8BR4zsw+L2l94BB5RtP1uMvtlIJslv13JBXZf+aJJmfIixFeZGYzJa1gHpzPcxheT6sPuFTSWPMAdWW2ldXMBi+wgaUsN+AqdWb0ZSna4JbbQG57nl4HIrXtJrPKk8W0u+or9B6Bt4nl8UHf8viA4xZ8kFfnoi2rn1bKqFQU5pNhHpJ0Y/K9lqLmlUrbMkjMg5R1GSS15QaS++sRPNDdmDSizMpKFCe97QJsKekgM6tKYc3PCXlWHjy8GbdQihMFqwKnDxfktsVHQ/14x7CF1U9ePJv6fP2MKZIupuVCK6vh1PS53CIvmLZrUjofwju5VfJCauXi98uDyrekc3e4OBKT0jWuKOksfE5KnjHJ9fA3Sadmvmh1xo2adkhj8Fpej0naOx1rITxrrciz8slhiwMLpw75aQoB1tSpnoxXF60qJQGtBIzfAR9MrpbNoKMOFqTsv9z/z+DZfWWzrruVqXjR0vwLSdsDN8hjHlVKvkm5jZXkKb5PS1rZPHV7BTqz2y7B60Y9hBeKvFrScxRijPQ+EJkkdxFXtZsrUnv9opk9aYVqwwXG4Blh90s6KnO5Fgd0ZvYHudtyG7wd9ON9XlnRxA5GpaJQrkS2fP2BqhLZE/EO6+Qkczje+C+mvRMvZpA8g+e5V2WQFMsNtL3wSkXxyqjxz34cf5k+iLuUPlLYfy+eRfEdeVmQc4GrrTP28tH0+0nc8skClG2doVUHTs8ryA0Ah8n9rpdRkz6b6Javn/FFfDS+Oj6f4coSmUbPxcwOlLQFnluPmU1PCqUYG7HC7wGqax5hZodKei+uxP5c9IUDv5J0HbCVteZxnEanyd+0Q/oG3rbWNE9RBXfHlVU93QmPp12Du+eOxJ95R5s1s+9X3WOOCYXPPI3Payh7Lk2z/6B7mYqH5K7MI8xshqQPpHsqK4QIzcptHIC7mxcAdpD0XXxA0iZrZifIg/XZiH9rPLW9OO+h0UAkd9ys3fye8nbzG9zdOUnSj4DzzKyqSu8EvATQ2mZ2OkC6ljKlfxn+fryJ1rydRozKYHaGpO8Dh5hnvawAfNvMPpLbf6OZbSHpOvNqjdn2W8xsk5LjZRkk4HnaxQDih8zsUnlc5Ela5QamWK60h3yCzap45543TwesYdCq5NoGA7PyPOpP4H7XV5rZawqyC+IKIJs9PtUq6tKrwZoZOdm1gE9aIdBXkPklrpgPxf26R5lX0C3KjUv7l8dTaP9gubVFCrJdM3vUXo7kYFzxlZYjScfbnVag/zwrZHupYXkTSeuY2V25/98B3FjSdpage4c0mJAhaZHkrlnJCmVDSj7zPtxyvsdK1glJI/ViPOGy/DUqzcUow8zaXF/p+9sLd/+Oo5X9d1bRvaUuZSpSm1ofHyzMTNteib/X++bkGq0JMVSkgcitOctiLLCnlaSfppH9VrS/U1/P7b/BzN6ZBrmfwQeJCwEPmdkHCsf6EB5zWixZr0havWzAmTvuBfiA4eayfq6MUWlR5GgrkV3SmHqqVIq7Ic7ARyKXSvqbtWeBHCLpT7Rnchj+AuZdO+/B/dSfrBkpAJ3zO/IUOvd81sg9eCdbdrxt8ODv/bQWT1lD7lct1s8HD/QtBLwav+9/kks/lccaDsaV4rczJaHq+llNixFegMc9Nse/z/MpzD6WTww7nVb5gn+m7fnyBai8HMkMKsqR4KO0h/BR4abpWgZTHSuOV1rexMzuKukYOkz+NBJtq7ck6SUrxG+SkvhKOvf++ASrsqqi2bVm2WW30jk7Ots/Bv+u87OKt6Ld+sie0+vw9nA7rbU4tihc4/P4DPtvy2dvr4KX6CgblXYrU3GOeYWAn5BmOadBVzGmlrm6lkn3cA8eN/g3bj10IM+s25vWc1nWzN6U219M1Mjf4zmF/2+UNC49m2xwU7QUMrqN7DM330x8/sOp8jTsMk/EIXh69vnySg996dqrlMVYPJNvgM7Z7ZWMdkWRL5H9djpLZPdSqRTcF78ZnjnyNXyUlFcU+QVH8g2pLZMjjQT3xPOoaxUFDed6WHWedZHD8dIdg5Vq02j2ejoXWgFP09xcXt/oC/giKHmalkvJzvOk+YI/2baq5R6XMbMLJH3CfNJkWWDwRLqXL4Dey5G8KneNV8grgv43x2tq8nert5SxfRd3TZ61c6PGk9UZ6F/LWmUlMn6mQiDdzD4KIM9c297MZklaAO8QS5HHfFbE3Zsv4p3aR/My1ipTsQoeGC9mwT0gL/2ylLxG2mB2lOWysjIrJCmUTyU31aIU5tQU+ArepvfEXX5bFvavgU8o/B4Fy7/ieF0HN7nr3TM/si/s7kgsSe/r70oO1Sh7LHE6PvH0WtzVXlo6pIzRrijyJbIvMbO2Wc/mmS279HC8OWb2pDxb4XkVMirM7EzgTEm7m1nHRKSC7B0Nz7kxzeZ6NCkOBt6pFjuq56hu/FmWTNWaGQtnoytJd+Ed6xYUMj7UewohSkXd5DnlZYkDL2WjpmQpXSefmVq8l6wcycPyACcqKUeSu7cHJb3VzG6XZ4sUR2aNjpenS8eQ0TR+M0fSQjl3TV0pnm7rhIxRmleQbUhupo4qqol8Bk4/PoipYhPz+UK/NrMLJQ1aj/LsKeFt+wTS3CR5UcBBV5GZfSrJn25mVSXI83RbEyLPE2Z2m6Q9zWyCfDnVQczsS6kNXmVmxYBzGU0GN6T7qRzZW6o6W7QwKVT0TbKD2WMN+pzL1Zo8e2l+sNiNUako5Jko+5qnV/6CmlFPjzwgLweyjHz+RdXEqdvki8BUrbH7Xaon0e1a+L/pXI8mxcHAR62/l8dJsjUSNsFzyMv4serXzGhULoXeUwi/iCuRNfGXpCyNuGv5gkRVOZJixofRsti2kPRCOlaxw256vEEamvxNF5NqUlW00ToheMD7JHkWTh8eu/k9hXIkOc4H/ihpKp6W+ZUKOfC2MRYvkbEA7cr+VHwgdzruotkHH31PpODKAsiUhKQDc4OMMmrXhCjwQlKKWe2usjTUT1FTi6lIg8EN+D3vS/eRfVMLkzoloYrJsyqvOlzKqFQUuJvpanlJ3QlWUeM+uQqKnUplaW7cRN0Nf7D/ofplypaArJr0c0n6vRfuO56MB4w3KDuYNZvr0Wh0bWbnJlN/A1rlBo6xwjoakj5vZqfhL9wU83TfX+BlofN8kWblUhqlEEqSOVNprQNeRZPyBW3lSHLbOoKzlmasdqPp8XI07RgaxW8K7pq/WMl6HeZFCLtiXupj+yaySf50uTt3DcpdRXlOwmc8j8djFCfl9r2YntcSZpZ15lfIZ/APkhRY1kb6gHdIWiddS0d8ycwOk8/OfiP1CzCBf79r4C7lr1JQempVePgg9dWlM5oMbjCzy3PnqBvZd7Uw1VlePX+ebHJxL1WHSxmtiuIhPPB1NPCHZGFkC3XkH8rBeGe+Iy0XSylqlU44Bx9lzKbaPK+d9GMp80TS/rnR0WR5KmXZuZssktN0dA1u5r+Al1Y+CdgpjfDzx9sjdeTHAQeqVZJ7eXIzpM0zeraQtLBaGT4z8FLZeZqmEP4xWSXHWMkiMnnSszwq+1+t8iE7lMnLg5efp1VapS142StNj9ekY1AP8Zvktjkb/47/Ji9Vc09BpsnsaEqU9Uu4VfqC5Wo7JctgDzw4PBmP7c2W9DkzK53UamaXSfoVXsb/wYJSeUieQv5LSUfiFtk2dKbQTsVn6B+JWztrUNNpy9eteH+6hzXT9e1RJmueSJLFCD9YIlJZ4cHK17h4FB+8TLfc2hu5a1sGtw7+haev/hhfw2Zn8yWUizSxMB/DFd5xdF9aoNvk2UpGq6IYMLOn8DUfxuMFwo7AswYGX2Qz+20aHb3ZWnnpHai30gnQfI3dxeRFBm/HJ9F1ZDip+SI5jUbXPRzvcLzDzdbeyCiuwZFxEd7x34X7nj9CLgPIms9lyFxit0v6Nh5bKp3wpoblQ3LUBi8lXUTFy1Y2eu12vIrjlCmJXuM3p+AZc3+Sp0KfQecaDl1nRyfWwO/5dHyQMUVelLE4Gp6Az995Ez4C3wMfOJ1Ge+mX4v1Op3NCGXjndgBeXHFZXBlMonMOzHHyulCfT+ecbjXlQ+i+bkUvmYQ74jHCXhfbWlwen/ic5Wq84c/jh3gSy3V4YsxMvNxK2ci+q4VpXsV3PeCfVlIbLNFL1eFSRquiGHSjmAesz0w/HZjZtxocr5fSCdB8jd1dcZP3VHxORXESHTRcJKdkdJ0VByuOrpse7wrcFbCdlU92K/JqMzsrffabKskAsmYphHPMJzpdgmdoHCrpz7g1WFxrunH5kERt8BJ3FxxHdcpuT8frQfH0Gr95zloVgu+RVDYHptHs6EwJS3qdmU1J2+6UOlZ1Wt7Mdkqj0HvM7Ffpcx2j0ibuEPPMv+8A37JCIT28blH+M1fJ1x3/Hv6865hpZl+X9Hoz21UeKyzSNJPwIdzyudnaC2YW21jTxbYWs7Q+hrxce1aep2P9lHT+bhV9M3anPpEiP3n2WWqqDlcxKhWFpVS+JsgnIz2f18aStk8dZUaj0gmpcd5vXj57Ej7i+l2ZGZqu815Jh+Gm+R/oLKMBrUDbsqRFcvBRS9siOWpWHKyX422TrutKeXDw9Xj5jj2q/L5KudvyNYaL5ScyuqUQZjnkjwD7J/fEWlBaKqLX8iG1wUvzulGb46u5XVpznEbHo7niaRq/ySyolySdQav8SpmPu2x2dJlrJ+MpSV+llUr+UGH/S5I+bmYXKRWyTBZimfuiqzukVysquXJ3B9aXtL55KZEyuq5bQcNMwhy7yzO2FsLLfNxHS7FD88W2npTPwTnOzN4FIOkTFFxKylWWSOf7Oz6PaZq1V5YABuetPJ8++2YrXwd7PTrXgq917eYZlYqiB7/rGbi/v1/SfsAH0ghrH9pLNxxPs9IJZwPvlGcpbYz7IT8jz47p6MAkfR43b5fGTfvX0+nKqlokpzjXo2txsMReuMvkTmoW3cGtkx3wmMwR5nWU1sYzbsqCzPsCP5KnYP6DTpdSRrcUwuPz/5gnItyTfijZ10v5kL1whVMavEzHrFwOs+J4lcHQHhRPMX7zf3iGWzF+8wZ8IJC5M4Qrgbvo5PO4y/BAPEZRujaKWhOzPo5nQG2NW7fFNZw/jsf0LrJWCfcPUfKcG7pDerKi5DWRHjGzYyWdLOmTZrZPiejReLutXLfCmmcSZrwXzxj7Nh7TO6Owv2yxra3oXGzrY8Du1p5csyKd61YsD6CSyhLFC1OrdlvGN+U13AaTYNRwLfg6RqWioLnf9c2WJiPJq7H+EG9kxca6Av5FvxXP3gDYzlK+dgnb4ov8zEoN/Ca8ARfZCfct32BmJ8vLGbRhuUVy0nX+2goZSommxcHuyh+PVgZWkRfMA16DJRrM7O5Oj8QgD+JK70lrL6jYgWpSCM3sGnkgPj/6uc7MinWP8p+5XJIBn5S0YNlIKbkC3paOuSj+wpZaernPrA8sWdbZSVrGzP4hXyp1HVyZ/qnk2poonp/jz+/59JmnJG2C++TzrGsN1zHBS15slhRB3SI+E/D42Petfb5NG6nN7VfYVjevodYdQu+F9N5iad0SM9tHBbevNJgtdzOteSqVczysc22T0naTeMK82uu4ZNm0FQ+09sW2lsWV8kFm9vuC3ExJP0gDmyzB4ELLJRcU6FZZAjw1eg5eBr6PVkwxH0tsuhZ8JaNSUVhzv2u/0gIoZnaqpJUklc0n2APvCI/LtHU6frHBLZcazKN46umTwCuofmGyTjx7OcoWYilO25+oNFXf2qfoX0iD4mBNrS3cgjoNz8Y6H+/MtsXLBeSPtwGukGfjI947ks/18+Yr9xWpTSGUVzJ9Gs+C2S79vY2kjc2sOMrNPrM78AYz20/StZK+Z62USyTtio+Ib8frX92Bu7VOzlmIqPm6EKfhvuus87wZ+LKky8zshLJrTJ+rUjwn4NVbr5H0bvyFn03nS1y2jgkA1pnO/VxD2eLMZyhfj6InMneIPNi+Oh4Puzcn0lMhPdyltIyZPSGf3Fjsuxpny2Xk2w1uFbS1mxyPpDb0bDrH4iX3e6d8rspW6dreJGkra6/htBseoL4Fd/euhZf9Od9SfK9At8oS4J6L04DJ5mnTvzazYuxtMbUWUapbC76SUakocnTzu34Hn7i0kXnQ+0Dc1VLMIGmaAXQ+nnm0FrB36vSm4qULyrgY72ReKy+WV1ZC43o8MyIrX5CVBymWBTld0iWWW8EPN1uLs4qbWltfwutVZfGMD+MNtehuOx4fsTwhXzVvXzx1ciLlGTv349kgd6aOuehSWsdaJSWulnSlmW0nnyBYxV74qBhcmd1M+0SrT+Pxi4E0GjwPz9S5ES8/ktF0XYh1zddmuBnY1HwGcD/uEhpUFE0VD54BcxfuhjDayzDkl+TNr2OyF/XpkFs3kbWGM5/VfIGj/GcOT9cxBVfMP7JUJM+aZ8FlHA38Tr6e+ZJ0ttfG2XI5urWbjD3w5Wkvxd1zVQHmS6kv1bILXgBxUJHJY52TcZdukdrKEjBYD2pXSftLOpPyPj1bC346PnCtWgu+ktGuKLr5XZfFH+pb8EDRAB64ahvJWSEDSL4E51MFXyNmVuZDXMsqJtSY2WnyPPO1/N/SINT6eCM608yuU836uVaYeFWiJBpbW0nhXJg6yQ3M7GL5+gYr0K5wx+XO+zBeW+iRNGIpI0ujvRMfaX6Y9kJ6Y5VmPEvaFLf6XkX97NjZaQSLmb0kqei+WJLW5MJF8TIPL5VcY9N1Ifrk82r+io/Knk3HL3bGjRSPmR2Gx1qOMLOvVt1keiaPJNfFbvigYSolZcbNiweuhHd+n6EVwGyz8tSqYtCtPEbTBY7ybIOX8ZiTFGlxjebpuBXeVtG3cH3Hpe+nH0+uWBZfLqD4jHvJlsvo1m5I++bQSiU9te6Grb5Uy4J4R523eBahM2lhbPr88/ikwV+k7W1LOxfOe6I81b7M2hlcCz4NiKHlQm/EqFQUaq329iz1WrPUpVTDM/LSBaXVYyUti5dx2JhWtsJkScea2X9ycmVlD9aUtIOZHZPfaJ5p9WHgBHm+dinqodJlIm9tbUSntZVxIb7KGLgb63zcfZNxa7KGrsGDftckE7uq2GG3NNo9gXPk8Yu/4CnEH6VTyee5Qp5lNgUPUBbXkPgW7pa7Ex+dfUnSsXRacE3XhTgGjzvdk457O8mNUJBrpHjy7aHYNortITEB77hKq9um4+QDmBtTHcDMqhicgvvLqzrLpgsc5XkMfw/+g2cMZZ1UZm00qei7fXKJfQG36LPPF9t1L9lyGbXtRp2B4kGsolqC6ku1HIO7Zu+nVT5nNdx6zzMRr4DQj7votjLPzvoIhcmG8vhOnmuzbWY2Mclkky//nd7VH+MTjKsm+nUwKhUFDVd7o+VSWg6fsJafnV3WEL5K9+qxp+GNenvclfBgksnPkciC0Tuk/VkJj9Ka+uaB6X0lfZpqd0OvlS4za+u9eCdyeIUcZnZj+n2zOoPj+8hTc98AnJSsntdTU2NHNWm0ZnYn8Nb0wq2Z5N6AK6Kq6ztWviKd8M6uaJktiacHvhEvrPaUpKussMaElS9U86SZFS2Aq1IHsxEeu3kCuMM6y1k0VTw9tQe6V7eF5gHMh2hVMdhf1VUMsGYLHCHpNrzdLQfcL+luvH3kLd6mFXh3wcvyL4TPRM/adjHu1zhbLieTtZvVKS/3sTtu0f+azneqrH/oVp11ZVx5b4S7I5/BFy4qVoVYzsw+DCBfie9n8nLtZe9+Fld8G+7qupXW+jFZxlevE/06GK2Koulqb1PxhjgW/8LG4PMZ9qOc2uqxuGmXpdv9UNK1ZvYeSfvnhTLzUdIHzCzztV6k6hIeG9HKArpcnqbb5kKwhpUu1aoUmrkPFqakFIm8Bs/TuOXxWVpLks4oyF0M7JeZx+la7q86P556/CN5kP5PdGb2ZHyfehcV8tTa9+MdreHxkffIUy3zmWGDliO5ciQqJCOofWncg/BO4o+SplpnJtfm+Pd2GZ4y+UkVyqBUKJ6OBYmatge1ZhQ/pPrqttA8gNmoikGPNJkk1rQC76twX/2KwH1mdkmSvSEvZK2yOMvgk12zRaeOLz67wnPekVbl2ocKsjslmW+YlZcpKVzD5blrKCvV0tYO8fd5+WI7BBaStKyZPW6eYv1aPJ7ZUY7HzA5J57zazAZX5ZSUP15PE/3KGK2KYsDMHsTN1jeTlhGVVFzt7Vy8JPfg7GV5TZXv4uZ6kW7VY2ek7VfhFsUj8oBdFcvIYwV/SZ18h39RveVAf5Lui5EcjZv83yQpHtyVdAo+ryLjSnzk8e90/h3wEg7FjIq34wvT17ou1Mreegq3riam662i60xv3F+8KN6ZLIOb5Zn7YrucXFPLsdHSuGpYBiV1vAfhvuZvW5rhrupFnbq1h6y6Lfh8nZn4SLssaPt1mgUwS6sYqFDmohc3jJn9TRX1yXJiXyZV4MUD0K/Ga5MVZ3UfhscQ+3B378Kp06uyrCfiVt5E3C13IZ21v4rP+TDKl0BeFHfpNVrgRz5J9TS8jS8m6bOZNZ7IJ8XshLfZ6bhb8tqC3KTUd0zDg+SL46VEqlhO0pLJWl6G9sSDRhP96hitiiLfiKZa9dKcYy0FdDPM7DeqniswAe+sb8Hr1BSX8HwtPnnuJHwE/gW8AVZ1iPsAP5BnC/0NV2hFGrkQlEt7TddflfaasYG1Kp5eVdIRD6ZX4goC3GS+klamCDR3XRSzt15PK9Ojaj2KzEW1GuUzvdc2s01TpzY1ixfJ62zlaWo5zjYvM3KYmWUxn7vkMaI8jcqg0MOiTolu7WFLPNi7Pu66WBR3rZTN1bgQT0X+ac4l1hHAtNaCRHvivvKswOFLtK+o1tgN01CR7mJmb1V76eslyMUxEi9amu8iX7L1BnkaapVLdaz5ujDg8aOyYn9Nn/O/gC9Y+yqWdRwJbGg+oe9VeAxssCy45ZJicEW+AG6RPlQ4zsfNbM303UwmlQWnvpbYsXj8I/t/59y+lfDlUs+gVSXg1fjgoRGjVVHsJumneMOepVSfBs/uyLtF7pZnKBTLHJRlH4ErgJ3Ni7GdiCuOfHnpv+Od3mq4if9OWjVkBjvXNLr+Gr7q1z54h7I0Xosln2sOzV0ITdNeV0p+z6clrWxmD8lnfRaPl0+vLC2XnGjquijL3qpbH3wf3H33SryTKXVRyedXTJb7cElKpWiiN7Ucy5bG3ZbONMeyMij/oVAGheaLOjVtD9l9TMl9tsoCvhvPuPqYfAJmVV2yjN1wBXY4PoItKp9e3DBNFGnT0tcPyQs9HmG+at0H8HhV2zorOYv1cfmcjEm4q/TBkuvLnvMvuzznu4G3JDfX0VZfjBBgRrLKMK/g/GyF3AF4v5GVeIpGCcAAAAR3SURBVCnGOl9OWfDn8Xb1PN4O8337Y7hSWE2tOTV9uNejbDmFDkarojgXn0MwOIJKL9QE2l+oz+Gm4Ca4afcMbrZWVZKdZa1ibH8t8fHl89zrOtdz8cD4EngjXRs3V6+ntfZvxsHATcmFsDitsgxtLgRrPsnwADzDYwFgB/kiSrdRKFxoKRWTdlO8jDbXhXxy3gQrpFBa8+yt/ITAPrwDXgaPWRQtoz1wN9ytudjACXRmHzW1HItL487C20OxXEqxrMqN+OTKojJruqhT0/bQiwX8nPlcj/XxSV1npOP91czKJpU+bmaPymcf3yipmG3VixumUT2xRLfS17vi7sKBJPN3Se+g8xnns4E+h1sy/6bc8sie80Z4p/w4Je8And/h6ZR8h2oVQeyXB8dvwQPKVfM4slgnVh7rzOilLHidNdO0X6pktCqKsda5ZGDHC5X86T+hWjEU+VtqFFlgty0FtIfOtd/Mrk8jrq+Z18VHUtmM0r/g1tC78AZ9OfUB926TDC8Cvmjts5xL1z1oyFGSLsNHw6fgo+F+SYdYe/XSfPbWzlQv39nUMiIp7R0l7Skpc5uMwdNFX58TbWo5LkVrlbq+dC+HUwiwWiqrktw1x+CJAYuUnPcLwCnyRZ0es+pFnZq2h14s4Cxd9HfAB+XrXWxGdbro0/IJggPJdTe+sL8XN0yTemKNSl+nNjOhsO3fdFo82US9rBLsRKoTJZbCv7s7cBfNRHzwtBHtirnpd5hZWM/gbeEpvJM+kXK6xTpfTlnwSmumh36pktGqKHp1KTVlF7xBboP77Y99mcd5SD4xqB/4j6Tj0nWWVfbM3A2DwdkadwN0n2R4N74gyw1AE5dEN86hNRr+OfXWEQBmNoHCy5/b19QyytPNbdLUcuw1jtLtvCvhM6tvTf7wH5rZ99U556Vpe+jFAp6Q/8c8m+fK9FPGbrjL9GDK1z5o7IaxBvXErL309UxeRunrAtfR/Nk1teAmFK659Du0VkbRFFqu6Unp8/lV/TLyK2U+S6EgZy/fTYU1swHV1szLYrQqil5dSo0wn8X5na6C3fk0rmzuw10r++ENYtcS2Z4C7tZ9kmGvLolu9GId9UI3yyhPrdukB8ux1zhKN3fNYXicYAyesTM2dSrFtZQbtYdeLOCs82qKeYHLO9O/+5eINHLDwOBciLKVFdvqTKVBQb5tl5WxaEovz65Rm+31O6S7azo77iy63GsP340VfkPnhNP/mlGpKF6GS2lYSQ0lX7el7MXMmNvWUa8uiW70Yh31QjfLKE83t0kjmsZRejjvi5bm7qgmY6fH9jBS9NJuGi8xPLfo8dkNVZutdU0PBS9Dmb0sRqWiGGXMbetoQv6fBi6JbvRiHTWmgWWUp5vbpJfzNomjND1vo4yd+YQJ+X/q2o01XGJ4btPDsxuSNsvcc03Pc/QNDFSlIwdB8N8gL4T3CXzuy8y07ZV4Rl4vCyQFwYgSiiIIgiCopZtpHQRBEPyPE4oiCIIgqCUURRAEQVBLKIogCIKglv8HDQaGiR7bD2wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['app_category'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-7b8196f1083b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'click'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[1;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, size, aspect, dropna, plot_kws, diag_kws, grid_kws)\u001b[0m\n\u001b[0;32m   2071\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"scatter\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2072\u001b[0m         \u001b[0mplot_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"edgecolor\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"white\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2073\u001b[1;33m         \u001b[0mplotter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2074\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"reg\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2075\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mregression\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mregplot\u001b[0m  \u001b[1;31m# Avoid circular import\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mmap_offdiag\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m   1491\u001b[0m         \"\"\"\n\u001b[0;32m   1492\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1493\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_lower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1494\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_upper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1495\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mmap_lower\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m   1423\u001b[0m                 \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpalette\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkw_color\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mkw_color\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1424\u001b[0m                 func(data_k[x_var], data_k[y_var], label=label_k,\n\u001b[1;32m-> 1425\u001b[1;33m                      color=color, **kwargs)\n\u001b[0m\u001b[0;32m   1426\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1427\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clean_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)\u001b[0m\n\u001b[0;32m   3468\u001b[0m                          \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3469\u001b[0m                          \u001b[0mlinewidths\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3470\u001b[1;33m                          edgecolors=edgecolors, data=data, **kwargs)\n\u001b[0m\u001b[0;32m   3471\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3472\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1853\u001b[0m                         \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1854\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1855\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1856\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1857\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[0;32m   4347\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ymargin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4348\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4349\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4350\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4351\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36madd_collection\u001b[1;34m(self, collection, autolim)\u001b[0m\n\u001b[0;32m   1895\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1896\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mautolim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1897\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_datalim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_datalim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransData\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1899\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_remove_method\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollections\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py\u001b[0m in \u001b[0;36mget_datalim\u001b[1;34m(self, transData)\u001b[0m\n\u001b[0;32m    207\u001b[0m             result = mpath.get_path_collection_extents(\n\u001b[0;32m    208\u001b[0m                 \u001b[0mtransform\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_transforms\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 209\u001b[1;33m                 offsets, transOffset.frozen())\n\u001b[0m\u001b[0;32m    210\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minverse_transformed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtransData\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\path.py\u001b[0m in \u001b[0;36mget_path_collection_extents\u001b[1;34m(master_transform, paths, transforms, offsets, offset_transform)\u001b[0m\n\u001b[0;32m   1007\u001b[0m     return Bbox.from_extents(*_path.get_path_collection_extents(\n\u001b[0;32m   1008\u001b[0m         \u001b[0mmaster_transform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1009\u001b[1;33m         offsets, offset_transform))\n\u001b[0m\u001b[0;32m   1010\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1011\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\path.py\u001b[0m in \u001b[0;36mvertices\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    206\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_nonfinite\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_vertices\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mvertices\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m         \"\"\"\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sns.pairplot(train, hue='click')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in train.columns:\n",
    "    print(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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